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  <front>
    <journal-meta><journal-id journal-id-type="publisher">ESSD</journal-id><journal-title-group>
    <journal-title>Earth System Science Data</journal-title>
    <abbrev-journal-title abbrev-type="publisher">ESSD</abbrev-journal-title><abbrev-journal-title abbrev-type="nlm-ta">Earth Syst. Sci. Data</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">1866-3516</issn><publisher>
    <publisher-name>Copernicus Publications</publisher-name>
    <publisher-loc>Göttingen, Germany</publisher-loc>
  </publisher></journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.5194/essd-18-6465-2026</article-id><title-group><article-title>GloPINE dataset: model-ready measurements of INP concentrations using PINE instruments</article-title><alt-title>GloPINE INP dataset</alt-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Herbert</surname><given-names>Ross J.</given-names></name>
          <email>r.j.herbert@leeds.ac.uk</email>
        <ext-link>https://orcid.org/0000-0002-2188-7136</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Lacher</surname><given-names>Larissa</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-1601-0276</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Böhmländer</surname><given-names>Alexander</given-names></name>
          
        <ext-link>https://orcid.org/0009-0007-3485-2139</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Tarn</surname><given-names>Mark D.</given-names></name>
          
        <ext-link>https://orcid.org/0000-0001-5827-4125</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3">
          <name><surname>Canzi</surname><given-names>Antoine</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff4">
          <name><surname>Pantoya</surname><given-names>Aidan</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3">
          <name><surname>Freney</surname><given-names>Evelyn</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-9363-9115</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Höhler</surname><given-names>Kristina</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Bogert</surname><given-names>Pia</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3 aff5">
          <name><surname>Planche</surname><given-names>Céline</given-names></name>
          
        <ext-link>https://orcid.org/0000-0001-8007-623X</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff6">
          <name><surname>Tian</surname><given-names>Ping</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-8795-5605</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Adams</surname><given-names>Michael</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff7">
          <name><surname>Barr</surname><given-names>Sarah</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-5185-2540</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff8">
          <name><surname>Brus</surname><given-names>David</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-8766-7873</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Büttner</surname><given-names>Nicole</given-names></name>
          
        <ext-link>https://orcid.org/0009-0004-3020-6525</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Daily</surname><given-names>Martin</given-names></name>
          
        <ext-link>https://orcid.org/0000-0001-8387-1394</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff8">
          <name><surname>Doulgeris</surname><given-names>Konstantinos</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-0579-0449</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff9">
          <name><surname>Eleftheriadis</surname><given-names>Konstantinos</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-2265-4905</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff7 aff10">
          <name><surname>Forster</surname><given-names>Grant</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-1783-9307</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Fösig</surname><given-names>Romy</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-8162-4354</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff11">
          <name><surname>Georgakopoulos</surname><given-names>Dimitrios G.</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-2223-8066</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff9">
          <name><surname>Gini</surname><given-names>Maria I.</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff12">
          <name><surname>Hallar</surname><given-names>A. Gannet</given-names></name>
          
        <ext-link>https://orcid.org/0000-0001-9972-0056</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff13">
          <name><surname>Krejci</surname><given-names>Radovan</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-9384-9702</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff14">
          <name><surname>Ludewig</surname><given-names>Elke</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff15">
          <name><surname>Mazzola</surname><given-names>Mauro</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-8394-2292</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff12">
          <name><surname>McCubbin</surname><given-names>Ian B.</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff16">
          <name><surname>Nenes</surname><given-names>Athanasios</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-3873-9970</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff17">
          <name><surname>Petäjä</surname><given-names>Tuukka</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-1881-9044</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Robinson</surname><given-names>Joseph</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-0146-0191</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2 aff18">
          <name><surname>Vogel</surname><given-names>Franziska</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-9605-5684</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff19">
          <name><surname>Zieger</surname><given-names>Paul</given-names></name>
          
        <ext-link>https://orcid.org/0000-0001-7000-6879</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Arnold</surname><given-names>Stephen</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-4881-5685</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Carslaw</surname><given-names>Kenneth S.</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-6800-154X</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff20 aff21">
          <name><surname>Hiranuma</surname><given-names>Naruki</given-names></name>
          
        <ext-link>https://orcid.org/0000-0001-7790-4807</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Möhler</surname><given-names>Ottmar</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-7551-9814</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Murray</surname><given-names>Benjamin J.</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-8198-8131</ext-link></contrib>
        <aff id="aff1"><label>1</label><institution>Institute for Climate and Atmospheric Science, University of Leeds, Leeds, United Kingdom</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>Institute of Meteorology and Climate Research Atmospheric Aerosol Research, Karlsruhe Institute of Technology, Karlsruhe, Germany</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>Université Clermont Auvergne, CNRS INSU, Laboratoire de Météorologie Physique (LaMP), UMR 6016, F-63000, Clermont-Ferrand, France</institution>
        </aff>
        <aff id="aff4"><label>4</label><institution>College of Engineering,  West Texas A&amp;M University (WTAMU), Canyon, Texas, United States</institution>
        </aff>
        <aff id="aff5"><label>5</label><institution>Institut Universitaire de France (IUF), Paris, France</institution>
        </aff>
        <aff id="aff6"><label>6</label><institution>Beijing Weather Modification Center, Beijing Meteorological Bureau, Beijing, China</institution>
        </aff>
        <aff id="aff7"><label>7</label><institution>National Centre for Atmospheric Science, Natural Environment Research Council, Leeds, United Kingdom</institution>
        </aff>
        <aff id="aff8"><label>8</label><institution>Finnish Meteorological Institute, Erik Palménin aukio 1, FI-00506, Helsinki, Finland</institution>
        </aff>
        <aff id="aff9"><label>9</label><institution>Environmental Radioactivity and Aerosol Technology for Atmospheric and Climate Impact Lab, National Centre For Scientific Research “Demokritos”, 15341 Agia Paraskevi, Athens, Greece</institution>
        </aff>
        <aff id="aff10"><label>10</label><institution>Weybourne Atmospheric Laboratory, University of East Anglia, Norwich, United Kingdom</institution>
        </aff>
        <aff id="aff11"><label>11</label><institution>Agricultural University of Athens, Department of Crop Science, Laboratory of General and Agricultural Microbiology, 118 55 Athens, Greece</institution>
        </aff>
        <aff id="aff12"><label>12</label><institution>Storm Peak Laboratory, Department of Atmospheric Sciences, University of Utah,  Salt Lake City, UT, United States</institution>
        </aff>
        <aff id="aff13"><label>13</label><institution>Department of Environmental Science, Stockholm University, 11419 Stockholm, Sweden</institution>
        </aff>
        <aff id="aff14"><label>14</label><institution>Sonnblick Observatory, Geosphere Austria, Salzburg, Austria</institution>
        </aff>
        <aff id="aff15"><label>15</label><institution>Institute of Polar Sciences, National Research Council, Bologna, Italy</institution>
        </aff>
        <aff id="aff16"><label>16</label><institution>Laboratory of Atmospheric Processes and Their Impacts, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland</institution>
        </aff>
        <aff id="aff17"><label>17</label><institution>Institute for Atmospheric and Earth System Research, University of Helsinki, Helsinki, Finland</institution>
        </aff>
        <aff id="aff18"><label>18</label><institution>Institute of Atmospheric Sciences and Climate, National Research Council, Bologna, Italy</institution>
        </aff>
        <aff id="aff19"><label>19</label><institution>Bolin Centre for Climate Research, Stockholm University, 11419 Stockholm, Sweden</institution>
        </aff>
        <aff id="aff20"><label>20</label><institution>Department of Life, Earth, and Environmental Sciences, West Texas A&amp;M University (WTAMU),  Canyon,  Texas, United States</institution>
        </aff>
        <aff id="aff21"><label>21</label><institution>Department of Physics, University of Texas El Paso, El Paso, Texas, United States</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Ross J. Herbert (r.j.herbert@leeds.ac.uk)</corresp></author-notes><pub-date><day>4</day><month>September</month><year>2026</year></pub-date>
      
      <volume>18</volume>
      <issue>9</issue>
      <fpage>6465</fpage><lpage>6483</lpage>
      <history>
        <date date-type="received"><day>19</day><month>January</month><year>2026</year></date>
           <date date-type="rev-request"><day>30</day><month>January</month><year>2026</year></date>
           <date date-type="rev-recd"><day>30</day><month>June</month><year>2026</year></date>
           <date date-type="accepted"><day>17</day><month>August</month><year>2026</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2026 Ross J. Herbert et al.</copyright-statement>
        <copyright-year>2026</copyright-year>
      <license license-type="open-access"><license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p></license></permissions><self-uri xlink:href="https://essd.copernicus.org/articles/18/6465/2026/essd-18-6465-2026.html">This article is available from https://essd.copernicus.org/articles/18/6465/2026/essd-18-6465-2026.html</self-uri><self-uri xlink:href="https://essd.copernicus.org/articles/18/6465/2026/essd-18-6465-2026.pdf">The full text article is available as a PDF file from https://essd.copernicus.org/articles/18/6465/2026/essd-18-6465-2026.pdf</self-uri>
      <abstract><title>Abstract</title>

      <p id="d2e555">Ice-nucleating particles (INPs) are a subset of aerosol particles that facilitate the freezing of supercooled cloud droplets heterogeneously and influence the radiative properties of supercooled clouds. The role of INPs in the Earth system remains unquantified in part due to poorly constrained representations of their spatial distributions and properties in global and regional models. In this study, we present a quality controlled dataset <xref ref-type="bibr" rid="bib1.bibx36" id="paren.1"><named-content content-type="pre">https://doi.org/10.5281/zenodo.16745514,</named-content></xref>, called GloPINE, comprising 70 000 h of INP concentrations measured using Portable Ice Nucleation Experiment (PINE) instruments that use an expansion chamber to make automated long-term (months to years) and high temporal resolution (<inline-formula><mml:math id="M1" display="inline"><mml:mrow><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">10</mml:mn></mml:mrow></mml:math></inline-formula> min). We collate measurements from 20 recent ground-based PINE field campaigns in the Northern Hemisphere conducted between January 2018 and December 2023, totaling more than 400 000 expansions, sampling over 800 m<sup>3</sup> of ambient air, and performed under conditions relevant for mixed-phase clouds. In the GloPINE dataset, we subset and average the PINE measurements across synoptically relevant time intervals of 6 h and 2 K temperature bins, providing 36 000 INP measurements. Combining PINE expansions over these intervals enhances counting statistics at higher freezing temperatures, decreases the lower limit of measurable INP concentrations, and provides an INP dataset readily applicable to model simulation data and meteorological reanalysis products. Together with complementary INP datasets, GloPINE provides a valuable resource for advancing model evaluation, improving INP source attribution, and informing parameterizations across the full temperature range over which aerosols influence ice formation in clouds.</p>
  </abstract>
    
<funding-group>
<award-group id="gs1">
<funding-source>Natural Environment Research Council</funding-source>
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<award-id>NE/S00579X/1</award-id>
<award-id>NE/T006420/1</award-id>
<award-id>NE/Y005376/1</award-id>
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<funding-source>Biological and Environmental Research</funding-source>
<award-id>DE-SC-0018979</award-id>
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<award-group id="gs3">
<funding-source>Agence Nationale de la Recherche</funding-source>
<award-id>ANR-21-CE01-0003</award-id>
<award-id>ANR-21-ESRE-0013</award-id>
<award-id>ANR-21-CE01-0003</award-id>
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<funding-source>Research Council of Finland</funding-source>
<award-id>337552</award-id>
</award-group>
<award-group id="gs5">
<funding-source>H2020 European Research Council</funding-source>
<award-id>871115</award-id>
<award-id>862565</award-id>
</award-group>
<award-group id="gs6">
<funding-source>LIFE programme</funding-source>
<award-id>CCA/GR/001747</award-id>
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<award-group id="gs7">
<funding-source>Directorate for Geosciences</funding-source>
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<funding-source>Bundesministerium für Forschung, Technologie und Raumfahrt</funding-source>
<award-id>57572852</award-id>
</award-group>
<award-group id="gs9">
<funding-source>National Science Foundation</funding-source>
<award-id>1749851</award-id>
<award-id>2054847</award-id>
</award-group>
<award-group id="gs10">
<funding-source>European Regional Development Fund</funding-source>
<award-id>5021516</award-id>
</award-group>
</funding-group>
</article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d2e591">Aerosol particles play an important role in the Earth System. On relatively short timescales, they influence fluxes of radiation directly via aerosol extinction and indirectly via modification to cloud radiative properties <xref ref-type="bibr" rid="bib1.bibx21" id="paren.2"/>. Aerosol particles can directly influence cloud properties by acting as cloud condensation nuclei (CCN), thus influencing the cloud droplet size distribution <xref ref-type="bibr" rid="bib1.bibx27" id="paren.3"><named-content content-type="pre">e.g.,</named-content></xref>, and/or ice-nucleating particles (INPs), which induce primary ice production at supercooled temperatures <xref ref-type="bibr" rid="bib1.bibx57" id="paren.4"><named-content content-type="pre">e.g.,</named-content></xref>. Anthropogenic activity has changed the concentrations and spatial distribution of aerosols, thereby modifying the properties of clouds and fluxes of radiation <xref ref-type="bibr" rid="bib1.bibx68" id="paren.5"/>. The radiative forcing associated with anthropogenic aerosols over the industrial era is estimated to be negative on the global scale (thus partially counteracting the warming due to greenhouse gases), but there is considerable uncertainty. The latest Intergovernmental Panel on Climate Change (IPCC) assessment report <xref ref-type="bibr" rid="bib1.bibx28" id="paren.6"><named-content content-type="post">AR6</named-content></xref> estimates an effective radiative forcing of <inline-formula><mml:math id="M3" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>1.3 W m<sup>−2</sup> due to aerosol changes with an uncertainty range between <inline-formula><mml:math id="M5" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>2.0 and <inline-formula><mml:math id="M6" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>0.6 W m<sup>−2</sup>; <xref ref-type="bibr" rid="bib1.bibx3" id="text.7"/> estimate a similar range between <inline-formula><mml:math id="M8" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>2.0 and <inline-formula><mml:math id="M9" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>0.4 W m<sup>−2</sup> (for the same confidence interval). The uncertainty range increases when viewed on regional scales <xref ref-type="bibr" rid="bib1.bibx68" id="paren.8"/>.</p>
      <p id="d2e694">An important source of this uncertainty is associated with the physiochemical properties of each aerosol species that define its ability to act as a CCN or INP. INPs are a relatively rare subset of aerosols in the atmosphere <xref ref-type="bibr" rid="bib1.bibx57 bib1.bibx46" id="paren.9"/>, yet they can have substantial impacts on cloud radiative properties over widespread regions <xref ref-type="bibr" rid="bib1.bibx58 bib1.bibx72 bib1.bibx34 bib1.bibx54 bib1.bibx65 bib1.bibx44" id="paren.10"><named-content content-type="pre">e.g.,</named-content></xref>. Our current understanding from laboratory and modelling studies demonstrates that there are likely key aerosol species that drive the global distribution of INP availability. This includes mineral dust, organically enriched sea spray aerosol, primary biological aerosol particles (PBAPs; including pollen, fungal spores and bacteria), volcanic ash, and carbonaceous aerosol <xref ref-type="bibr" rid="bib1.bibx57 bib1.bibx46 bib1.bibx43" id="paren.11"/>. Due to the short-lived nature of these aerosol species and spatially and temporally variable emission fluxes, it is necessary to use global climate models to quantify the role of aerosols and INPs in the climate.</p>
      <p id="d2e708">In situ INP observations are vital for testing the representation of INPs in global models. These are used to evaluate the model and identify biases in aerosol distributions and/or their ice-nucleating activity. For example, <xref ref-type="bibr" rid="bib1.bibx22" id="text.12"/> used the BACCHUS (Impact of Biogenic versus Anthropogenic emissions on Clouds and Climate: towards a Holistic Under Standing) global INP observational dataset to evaluate their INP model and found evidence that PBAPs may play a more important role than previously thought. <xref ref-type="bibr" rid="bib1.bibx35" id="text.13"/> used an alternative collection of observations to evaluate their INP model and identified a potential missing INP source from biogenic material associated with dust.</p>
      <p id="d2e717">These datasets provide unequivocally important observational constraints, but have associated limitations. INP measurements have been made using a wide variety of instruments and methods, each with their own uncertainties and biases. This is well demonstrated by intercomparison studies for ambient air samples <xref ref-type="bibr" rid="bib1.bibx26 bib1.bibx52" id="paren.14"/> and in laboratory based studies <xref ref-type="bibr" rid="bib1.bibx25 bib1.bibx79 bib1.bibx41 bib1.bibx42" id="paren.15"/> that regularly report a variation of around an order of magnitude (or more) in measured INP concentrations. Field campaigns are also often restricted in the duration and temporal resolution of INP measurements, either due to methodological constraints (e.g., filter samples taken on daily timescales) or physical constraints (e.g., 2 h time series taken during a daytime research flight). In addition, in many campaigns, the duration of the deployment is typically on the order of days to weeks. This restricts model evaluation to daily or monthly mean comparisons, which is likely an inadequate test for a variable that can vary by several orders of magnitude during a single diurnal cycle in certain conditions <xref ref-type="bibr" rid="bib1.bibx20" id="paren.16"><named-content content-type="pre">e.g.,</named-content><named-content content-type="post">Fig. 5a</named-content></xref>.</p>
      <p id="d2e734">Recently, instruments for automated and continuous operation have been developed, including the Continuous Flow Diffusion Chamber – Ice Activation Spectrometer <xref ref-type="bibr" rid="bib1.bibx4" id="paren.17"><named-content content-type="pre">CFDC-IAS;</named-content></xref>, the automated Horizontal Ice Nucleation Chamber <xref ref-type="bibr" rid="bib1.bibx15" id="paren.18"><named-content content-type="pre">HINC-Auto;</named-content></xref>, and the Portable Ice Nucleation Experiment chamber <xref ref-type="bibr" rid="bib1.bibx56" id="paren.19"><named-content content-type="pre">PINE;</named-content></xref>. These build upon previous instruments designed to make online in-situ measurements <xref ref-type="bibr" rid="bib1.bibx63 bib1.bibx45 bib1.bibx67" id="paren.20"><named-content content-type="pre">e.g.,</named-content></xref> with the additional capability of operating autonomously on long time scales (months to years) at relatively high temporal resolution (<inline-formula><mml:math id="M11" display="inline"><mml:mrow><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">15</mml:mn></mml:mrow></mml:math></inline-formula> min).</p>
      <p id="d2e767">In this study, we present a new INP measurement dataset designed for model evaluation using one of these new instruments. We collate ground-based INP measurements made using PINE instruments that reproduce mixed-phase cloud conditions from 20 recent field campaigns, providing over 400 000 individual measurements sampled over 70 000 h. We subset and average these PINE measurements into regular 6 h time intervals (consistent with synoptic scale changes in meteorology) and 2 K temperature bins to provide a model-ready dataset, which we name the GloPINE INP dataset. The new dataset includes around 36 000 INP measurements above the lower limit of measurable concentration (non-zero) from across the Northern Hemisphere, with a mean campaign duration of 172 d (ranging from 16 to 804 d). The interval-averaged measurements span a range of concentrations from less than 0.01 L<sup>−1</sup> to more than 1000 L<sup>−1</sup> and temperatures between 240 and 263 K. In Sect. 2, we provide a brief description of the PINE instrument and relevant information for each of the 20 field campaigns. In Sect. 3, we describe and demonstrate the methodology for subsetting and averaging each time series. In Sect. 4, we introduce the GloPINE INP dataset and provide an outline of the data format and public access, and in Sect. 5, we conclude the study.</p>
</sec>
<sec id="Ch1.S2">
  <label>2</label><title>PINE measurements</title>
<sec id="Ch1.S2.SS1">
  <label>2.1</label><title>The PINE instrument</title>
      <p id="d2e809">The mobile PINE cloud simulation chamber, described in full by <xref ref-type="bibr" rid="bib1.bibx56" id="text.21"/>, was designed to reproduce the conditions of an ascending air parcel cooling adiabatically. This is achieved through expansion cooling of a chamber using a mechanical pump thus reducing pressure, reducing temperature, and increasing relative humidity. The temperature of the walls of the chamber is controlled by a cooling system that is used to set the starting temperature of the chamber walls and thus the air temperature within the chamber (to a minimum of about 213 K) prior to expansion. Each PINE run includes three stages or modes: flush, expansion, and refill. In the flush mode, ambient air is drawn through the vessel at a flow rate around 2 L min<sup>−1</sup>. In the expansion mode, the inlet valve to the chamber is closed, whilst the outlet flow of air exiting the chamber is pumped at a rate of 3 to 4 L min<sup>−1</sup>, causing the air within the chamber to expand. Note that all flows and concentrations reported are at standard temperature and pressure. The expansion flow acts to reduce the temperature inside the chamber and increase relative humidity and continues to a predefined minimum pressure. The instrument is capable of reproducing conditions appropriate for homogeneous freezing and heterogeneous freezing through immersion, pore-condensation, and deposition modes. In this dataset, we focus on freezing via the immersion mode, which is the dominant pathway for primary ice production in mixed-phase clouds <xref ref-type="bibr" rid="bib1.bibx57 bib1.bibx24 bib1.bibx78" id="paren.22"/>. The humidity of the air being drawn into the chamber is controlled by two Nafion membrane diffusion dryers in parallel, which are set to keep the relative humidity high enough for cloud droplet formation to occur and low enough to avoid frost formation on the chamber walls. If excessive frost does form on the chamber walls, frost particles can become dislodged and counted as ice crystals by the optical particle counter (OPC). Regular zero checks are performed where HEPA filtered air is passed through the chamber (see <xref ref-type="bibr" rid="bib1.bibx56" id="altparen.23"/>, for details); the buildup of ice on the chamber walls is also avoided by regularly warming the chamber to remove any ice. The frequency with which the chamber is warmed varies between campaigns, depending on factors such as the humidity of ambient air and the flow rate through the dryers. Aerosols in the chamber can activate to cloud droplets once water saturation is exceeded, after which the droplets may freeze heterogeneously upon INPs present in the chamber. The hydrometeor size distribution (both liquid and ice) is measured with an OPC positioned downstream of the chamber in the pump line establishing the expansion flow; interstitial aerosols are too small to be counted as ice crystals. The PINE-01-A instrument uses a Palas GmbH welas 2500 OPC sensor, whilst all other instruments use a Palas GmbH fidas-pine OPC (see Table <xref ref-type="table" rid="T1"/> for a list of instruments used in each campaign). Ice crystals are optically larger than cloud droplets and are distinguished using a size threshold that is determined by post-processing software (see Sect. <xref ref-type="sec" rid="Ch1.S2.SS3"/>). In the refill mode, the chamber is re-pressurized to the starting pressure at a controlled rate. The total time for each run is around 5 to 15 min and primarily determined by the time for the flush mode. The cumulative number of ice crystals (<inline-formula><mml:math id="M16" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">ice</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>) measured by the OPC in the course of the expansion is used to calculate the cumulative concentration of INPs (<inline-formula><mml:math id="M17" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>) per liter of sampled air by dividing <inline-formula><mml:math id="M18" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">ice</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> by the total volume of air passing through the OPC detection volume. For the welas sensor (on PINE-01-A), this volume is 10 % of the total volume passing the OPC, whilst for the fidas-pine sensor (on all other PINEs), this volume is equal to the total volume passing the OPC. For additional details on the design and operating procedure of PINE see <xref ref-type="bibr" rid="bib1.bibx56" id="text.24"/> and <xref ref-type="bibr" rid="bib1.bibx18" id="text.25"/>; typical output for a PINE expansion can be seen in <xref ref-type="bibr" rid="bib1.bibx56" id="text.26"><named-content content-type="post">Fig. 3</named-content></xref>.</p>

<table-wrap id="T1" specific-use="star"><label>Table 1</label><caption><p id="d2e898">Details of PINE measurement campaigns collated in this dataset. The location is provided in decimal degrees, altitude in m above sea level, and dates in the format dd-mm-yy.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="6">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="left"/>
     <oasis:colspec colnum="3" colname="col3" align="left"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="left"/>
     <oasis:colspec colnum="6" colname="col6" align="left"/>
     <oasis:thead>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Name of campaign</oasis:entry>
         <oasis:entry colname="col2">Location</oasis:entry>
         <oasis:entry colname="col3">Altitude</oasis:entry>
         <oasis:entry colname="col4">Dates</oasis:entry>
         <oasis:entry colname="col5">Software</oasis:entry>
         <oasis:entry colname="col6">Instrument</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1">HyICE-2018</oasis:entry>
         <oasis:entry colname="col2">Hyytiälä, Finland (61.85° N, 24.29° E)</oasis:entry>
         <oasis:entry colname="col3">118 m</oasis:entry>
         <oasis:entry colname="col4">22-03-18–29-04-18</oasis:entry>
         <oasis:entry colname="col5">PIA 2.0.0</oasis:entry>
         <oasis:entry colname="col6">PINE-01-A</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">PICNIC</oasis:entry>
         <oasis:entry colname="col2">Puy de Dôme, France (45.77° N, 2.96° E)</oasis:entry>
         <oasis:entry colname="col3">1465 m</oasis:entry>
         <oasis:entry colname="col4">09-10-18–25-10-18</oasis:entry>
         <oasis:entry colname="col5">PIA 2.0.0</oasis:entry>
         <oasis:entry colname="col6">PINE-01-A</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">ExINP-SGP</oasis:entry>
         <oasis:entry colname="col2">SGP site OK, US (36.61° N, <inline-formula><mml:math id="M19" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>97.49° E)</oasis:entry>
         <oasis:entry colname="col3">314 m</oasis:entry>
         <oasis:entry colname="col4">01-10-19–14-11-19</oasis:entry>
         <oasis:entry colname="col5">WTAMU</oasis:entry>
         <oasis:entry colname="col6">PINE-03</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">ExINP-ENA</oasis:entry>
         <oasis:entry colname="col2">Graciosa, Azores (39.09° N, <inline-formula><mml:math id="M20" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>28.03° E)</oasis:entry>
         <oasis:entry colname="col3">30 m</oasis:entry>
         <oasis:entry colname="col4">01-10-20–28-03-21</oasis:entry>
         <oasis:entry colname="col5">WTAMU</oasis:entry>
         <oasis:entry colname="col6">PINE-03</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">CORONA-PINE04</oasis:entry>
         <oasis:entry colname="col2">Karlsruhe, Germany (49.10° N, 8.43° E)</oasis:entry>
         <oasis:entry colname="col3">114 m</oasis:entry>
         <oasis:entry colname="col4">25-03-21–02-05-21</oasis:entry>
         <oasis:entry colname="col5">PIA 2.1.0</oasis:entry>
         <oasis:entry colname="col6">PINE-04-01</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">CORONA-PINE01</oasis:entry>
         <oasis:entry colname="col2">Karlsruhe, Germany (49.10° N, 8.43° E)</oasis:entry>
         <oasis:entry colname="col3">114 m</oasis:entry>
         <oasis:entry colname="col4">09-04-21–29-04-21</oasis:entry>
         <oasis:entry colname="col5">PIA 2.1.0</oasis:entry>
         <oasis:entry colname="col6">PINE-01-A</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">CountIce-pt1</oasis:entry>
         <oasis:entry colname="col2">Leeds, UK (53.81° N, <inline-formula><mml:math id="M21" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>1.56° E)</oasis:entry>
         <oasis:entry colname="col3">66 m</oasis:entry>
         <oasis:entry colname="col4">20-07-21–15-10-22</oasis:entry>
         <oasis:entry colname="col5">PIA 2.0.2</oasis:entry>
         <oasis:entry colname="col6">PINE-04-03</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">SBO21</oasis:entry>
         <oasis:entry colname="col2">Sonnblick Observatory, Austria (47.05° N, 12.96° E)</oasis:entry>
         <oasis:entry colname="col3">3106 m</oasis:entry>
         <oasis:entry colname="col4">28-07-21–15-10-22</oasis:entry>
         <oasis:entry colname="col5">PIA 2.1.0</oasis:entry>
         <oasis:entry colname="col6">PINE-04-02</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">SPL01</oasis:entry>
         <oasis:entry colname="col2">Storm Peak CO, US (40.46° N, <inline-formula><mml:math id="M22" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>106.74° E)</oasis:entry>
         <oasis:entry colname="col3">3220 m</oasis:entry>
         <oasis:entry colname="col4">12-10-21–12-07-22</oasis:entry>
         <oasis:entry colname="col5">PIA 2022</oasis:entry>
         <oasis:entry colname="col6">PINE-01-A</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">CALISHTO</oasis:entry>
         <oasis:entry colname="col2">Helmos, Greece (37.9° N, 22.2° E)</oasis:entry>
         <oasis:entry colname="col3">2314 m</oasis:entry>
         <oasis:entry colname="col4">12-10-21–08-05-22</oasis:entry>
         <oasis:entry colname="col5">PIA 2.1.0</oasis:entry>
         <oasis:entry colname="col6">PINE-04-01</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">ExINP-NSA</oasis:entry>
         <oasis:entry colname="col2">North Slope, Alaska, US (71.32° N, <inline-formula><mml:math id="M23" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>156.62° E)</oasis:entry>
         <oasis:entry colname="col3">8 m</oasis:entry>
         <oasis:entry colname="col4">19-10-21–01-01-24</oasis:entry>
         <oasis:entry colname="col5">WTAMU</oasis:entry>
         <oasis:entry colname="col6">PINE-03</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">BEIJING</oasis:entry>
         <oasis:entry colname="col2">Beijing CLOUD Base, China (40.17° N, 117.12° E)</oasis:entry>
         <oasis:entry colname="col3">100 m</oasis:entry>
         <oasis:entry colname="col4">28-03-22–17-07-23</oasis:entry>
         <oasis:entry colname="col5">PIA 1.0.1</oasis:entry>
         <oasis:entry colname="col6">PINE-05-03</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">M-Phase</oasis:entry>
         <oasis:entry colname="col2">Labrador Sea (various locations)</oasis:entry>
         <oasis:entry colname="col3">18 m</oasis:entry>
         <oasis:entry colname="col4">19-05-22–25-06-22</oasis:entry>
         <oasis:entry colname="col5">PIA 2.0.2</oasis:entry>
         <oasis:entry colname="col6">PINE-04-03</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">PaCE22</oasis:entry>
         <oasis:entry colname="col2">Pallas Sammaltunturi station, Finland (67.97° N, 24.12° E)</oasis:entry>
         <oasis:entry colname="col3">565 m</oasis:entry>
         <oasis:entry colname="col4">23-09-22–23-12-22</oasis:entry>
         <oasis:entry colname="col5">PIA 2.0.2</oasis:entry>
         <oasis:entry colname="col6">PINE-04-01</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">PUY</oasis:entry>
         <oasis:entry colname="col2">Puy de Dôme, France (45.77° N, 2.96° E)</oasis:entry>
         <oasis:entry colname="col3">1465 m</oasis:entry>
         <oasis:entry colname="col4">15-12-22–19-03-23</oasis:entry>
         <oasis:entry colname="col5">PIA 1.0.1</oasis:entry>
         <oasis:entry colname="col6">PINE-05-01</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">CountIce-pt2</oasis:entry>
         <oasis:entry colname="col2">Leeds, UK (53.81° N, <inline-formula><mml:math id="M24" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>1.56° E)</oasis:entry>
         <oasis:entry colname="col3">66 m</oasis:entry>
         <oasis:entry colname="col4">09-03-23–15-08-23</oasis:entry>
         <oasis:entry colname="col5">PIA 2.0.2</oasis:entry>
         <oasis:entry colname="col6">PINE-04-03</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">ExINP-GVB</oasis:entry>
         <oasis:entry colname="col2">Gruvebadet, Svalbard (78.92° N, 11.93° E)</oasis:entry>
         <oasis:entry colname="col3">67 m</oasis:entry>
         <oasis:entry colname="col4">15-03-23–10-04-23</oasis:entry>
         <oasis:entry colname="col5">PIA 2.0.2</oasis:entry>
         <oasis:entry colname="col6">PINE-04-02</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">LIFE-FROSTDEFEND</oasis:entry>
         <oasis:entry colname="col2">Aigio, Greece (38.23° N, 22.13° E)</oasis:entry>
         <oasis:entry colname="col3">20 m</oasis:entry>
         <oasis:entry colname="col4">27-03-23–24-04-23</oasis:entry>
         <oasis:entry colname="col5">PIA 2.1.0</oasis:entry>
         <oasis:entry colname="col6">PINE-05-02</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">ExINP-ZEP</oasis:entry>
         <oasis:entry colname="col2">Zeppelin, Svalbard (78.9° N, 11.88° E)</oasis:entry>
         <oasis:entry colname="col3">474 m</oasis:entry>
         <oasis:entry colname="col4">26-04-23–23-10-23</oasis:entry>
         <oasis:entry colname="col5">PIA 2.0.2</oasis:entry>
         <oasis:entry colname="col6">PINE-04-02</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">WAO</oasis:entry>
         <oasis:entry colname="col2">Weybourne, UK (52.95° N, 1.12° E)</oasis:entry>
         <oasis:entry colname="col3">17 m</oasis:entry>
         <oasis:entry colname="col4">27-10-23–01-01-24</oasis:entry>
         <oasis:entry colname="col5">PIA 2.0.2</oasis:entry>
         <oasis:entry colname="col6">PINE-04-03</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

      <p id="d2e1432">The temperature dependence of <inline-formula><mml:math id="M25" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is measured by changing the wall temperature of PINE, controlled by the cooling system. In this study, typical temperature scans are performed between 240  and 263 K. Post-processing software is used to extract <inline-formula><mml:math id="M26" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> and associated temperatures during each run and is discussed further in Sect. <xref ref-type="sec" rid="Ch1.S2.SS3"/>. <xref ref-type="bibr" rid="bib1.bibx56" id="text.27"/> estimate a temperature uncertainty of <inline-formula><mml:math id="M27" display="inline"><mml:mrow><mml:mo>±</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:math></inline-formula> K, primarily due to the inhomogeneous temperature distribution within the chamber. The uncertainty in <inline-formula><mml:math id="M28" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">ice</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is dependent on the OPC. For the PINE-01-A instrument using the welas OPC, <xref ref-type="bibr" rid="bib1.bibx56" id="text.28"/> estimate a conservative value of <inline-formula><mml:math id="M29" display="inline"><mml:mrow><mml:mo>±</mml:mo><mml:mn mathvariant="normal">20</mml:mn></mml:mrow></mml:math></inline-formula> %; for all other instruments that use the fidas-pine OPC, <xref ref-type="bibr" rid="bib1.bibx11" id="text.29"/> estimate a conservative value of <inline-formula><mml:math id="M30" display="inline"><mml:mrow><mml:mo>±</mml:mo><mml:mn mathvariant="normal">10</mml:mn></mml:mrow></mml:math></inline-formula> %. In our dataset, we use counting statistics that can be used to flag the most uncertain data; this is discussed further in Sect. <xref ref-type="sec" rid="Ch1.S3"/>. <xref ref-type="bibr" rid="bib1.bibx56" id="text.30"/> assessed the aerosol loss rate between air inlet and introduction to the PINE chamber and reported a loss of less than 20 % of particles with diameter <inline-formula><mml:math id="M31" display="inline"><mml:mrow><mml:mi>D</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M32" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula> and about 50 % for <inline-formula><mml:math id="M33" display="inline"><mml:mrow><mml:mi>D</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">4</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M34" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>. <xref ref-type="bibr" rid="bib1.bibx48" id="text.31"/> and <xref ref-type="bibr" rid="bib1.bibx80" id="text.32"/> also evaluated the loss rate in PINE during two field campaigns and report losses of 50 % at <inline-formula><mml:math id="M35" display="inline"><mml:mrow><mml:mi>D</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">9.4</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M36" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id="M37" display="inline"><mml:mrow><mml:mi>D</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">8.0</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M38" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>. <xref ref-type="bibr" rid="bib1.bibx1" id="text.33"><named-content content-type="post">Fig. 24</named-content></xref> assessed the transmission efficiency of size-resolved ambient air samples during the HyICE-2018 campaign <xref ref-type="bibr" rid="bib1.bibx77 bib1.bibx13" id="paren.34"/> and reports a drop in efficiency for <inline-formula><mml:math id="M39" display="inline"><mml:mrow><mml:mi>D</mml:mi><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">5</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M40" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>. This upper size limit will not greatly affect the measured <inline-formula><mml:math id="M41" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> but will have implications for reproducing the INP measurements from modelling studies. Following <xref ref-type="bibr" rid="bib1.bibx1" id="text.35"/>, we recommend that the upper size limit of <inline-formula><mml:math id="M42" display="inline"><mml:mrow><mml:mi>D</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">5</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M43" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula> is applied to simulated aerosol size distributions when comparing to the measured <inline-formula><mml:math id="M44" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>. We plan to assess the impact of this upper limit on modelled <inline-formula><mml:math id="M45" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> in a follow-up study.</p>
</sec>
<sec id="Ch1.S2.SS2">
  <label>2.2</label><title>Campaigns</title>
      <p id="d2e1709">In this dataset, we collate <inline-formula><mml:math id="M46" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> measurements made with PINE instruments from 20 campaigns that took place between 2018 and 2024. The locations and durations of the campaigns are shown in Fig. <xref ref-type="fig" rid="F1"/> and details of the campaigns and instruments are presented in Table <xref ref-type="table" rid="T1"/>. The campaigns include locations in the Arctic, Europe, North America, the North Atlantic Ocean, and East Asia. These locations are influenced by a range of important INP sources including arid environments (Central Africa, the Middle East, and Central Asia), marine environments, remote regions (the Arctic), and biologically rich environments (Europe and North America). The collated datasets provide <inline-formula><mml:math id="M47" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> on monthly, seasonal, and annual timescales. The shortest campaign duration is 16 d, the longest is 804 d, and the mean is 172 d. A brief characterization of each campaign is provided below and includes the mean volume sampled per expansion during each campaign. The volume sampled is calculated as the inverse of the minimum measurable value of <inline-formula><mml:math id="M48" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> (per liter of sampled air per expansion) during the campaign, which is consistent with a measurement of one ice crystal (per expansion); see Sect. <xref ref-type="sec" rid="Ch1.S3"/> for more details.</p>

      <fig id="F1" specific-use="star"><label>Figure 1</label><caption><p id="d2e1754">Location and duration of the PINE campaigns included in this dataset. All campaigns remained at the same location for the duration except for M-Phase, which was onboard a research ship. The ship trajectory (green line in the North Atlantic) is shown for the period when PINE measurements were being made.</p></caption>
          <graphic xlink:href="https://essd.copernicus.org/articles/18/6465/2026/essd-18-6465-2026-f01.png"/>

        </fig>

      <p id="d2e1763"><italic>HyICE-2018</italic>: INP measurements were conducted at the SMEAR II (Station for Measuring Ecosystem-Atmosphere Relations) station <xref ref-type="bibr" rid="bib1.bibx33" id="paren.36"/> located in the boreal forest in Hyytiälä, Finland. Aerosols were sampled through a heated total aerosol inlet 6 m above ground level. PINE was set to the following operation parameters: (1) Flush at 3 L min<sup>−1</sup> for 4 min, (2) expansion at 4 L min<sup>−1</sup> to 700 mbar, (3) refill at 3 L min<sup>−1</sup>. The mean volume of air sampled during a single expansion was <inline-formula><mml:math id="M52" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">2.0</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup>. More details on the campaign can be found in <xref ref-type="bibr" rid="bib1.bibx13" id="text.37"/> and <xref ref-type="bibr" rid="bib1.bibx77" id="text.38"/>.</p>
      <p id="d2e1844"><italic>PICNIC</italic>: INP measurements were conducted at the Site d’Observations Atmosphériques Puy de Dôme, a mountain-top site in France. It is an observational facility of the ACTRIS (Aerosol, Clouds, and Trace Gases Research Infrastructure) and the Global Atmosphere Watch (GAW) measurement programs. Sampling was performed via a heated whole-air inlet situated 2 m above the laboratory roof. PINE measurements were conducted with the following program: (1) Flush for 5 min at 2 L min<sup>−1</sup>, (2) expansion at 3 L min<sup>−1</sup> to 650 mbar (ambient pressure 850 mbar), (3) refill at 2 L min<sup>−1</sup>. The mean volume of air sampled during a single expansion was <inline-formula><mml:math id="M57" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">1.5</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup>. More details on the campaign can be found in <xref ref-type="bibr" rid="bib1.bibx52" id="text.39"/>.</p>
      <p id="d2e1918"><italic>ExINP-SGP</italic>: PINE measurements were made at the US Department of Energy (DOE) Southern Great Plains (SGP) Atmospheric Radiation Measurement (ARM) site in Oklahoma, US. The site is surrounded by farmland. The PINE instrument was housed within the Guest User Facility, with ambient air sampled through a 0.15 m diameter quasi-laminar sampling inlet with a total suspended particulate (TSP) sampling head at 5.5 m above the ground. The measurement program was the following: (1) Flush for 5 min at 2 L min<sup>−1</sup>, (2) expansion at 3 L min<sup>−1</sup> to 750 mbar, (3) refill at 2 L min<sup>−1</sup> to ambient pressure. The mean volume of air sampled during a single expansion was <inline-formula><mml:math id="M62" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">4.0</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup>. More details on this campaign can be found in <xref ref-type="bibr" rid="bib1.bibx80" id="text.40"/>.</p>
      <p id="d2e1992"><italic>ExINP-ENA</italic>: PINE measurements were made at the US DOE Eastern North Atlantic (ENA) ARM site on Graciosa Island, Azores. The PINE instrument was housed in an air-conditioned container on the site, with ambient air sampled through a 0.1 m diameter quasi-laminar sampling inlet with a TSP sampling head at 5.5 m above the ground. The measurement program was the following: (1) Flush for 10 min at 2 L min<sup>−1</sup>, (2) expansion at 3 L min<sup>−1</sup> to 800 mbar, (3) refill at 2 L min<sup>−1</sup> to ambient pressure. The mean volume of air sampled during a single expansion was <inline-formula><mml:math id="M67" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">2.8</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup>. More details on this campaign can be found in <xref ref-type="bibr" rid="bib1.bibx80" id="text.41"/>.</p>
      <p id="d2e2066"><italic>CORONA-PINE04 and CORONA-PINE01</italic>: The CORONA campaign was conducted at the AIDA (Aerosol Interaction and Dynamics in the Atmosphere) facility at the Karlsruhe Institute of Technology (KIT), Campus North in Germany. PINE was installed at a PM<sub>10</sub> non-heated aerosol inlet, as icing during winter was not expected. The inlet was installed 1 m above the laboratory roof and 9 m above ground. INP measurements were alternately conducted with the PINE models PINE-01-A and PINE-04-01. The measurement program was the following: (1) Flush for 3 min (PINE-01-A) or 4 min (PINE-04-01) at 2 L min<sup>−1</sup>, (2) expansion at 3 L min<sup>−1</sup> to 820 mbar, (3) refill at 2 L min<sup>−1</sup>. The mean volume of air sampled during a single expansion was <inline-formula><mml:math id="M73" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">1.5</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup> (PINE-1A) or <inline-formula><mml:math id="M75" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">2.3</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup> (PINE-04); the different volumes sampled per expansion reflect chamber volume in each instrument (<inline-formula><mml:math id="M77" display="inline"><mml:mrow><mml:mn mathvariant="normal">7</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup> for PINE-01-A and <inline-formula><mml:math id="M79" display="inline"><mml:mrow><mml:mn mathvariant="normal">10</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup> for PINE-04-01). The datasets are published <xref ref-type="bibr" rid="bib1.bibx49 bib1.bibx73" id="paren.42"/>, but no accompanying papers are currently available.</p>
      <p id="d2e2233"><italic>CountIce-pt1 and CountIce-pt2</italic>: PINE measurements were made within the Leeds IcePod mobile laboratory <xref ref-type="bibr" rid="bib1.bibx59" id="paren.43"/> located on the University of Leeds campus, UK. The site was characterized as an urban environment with low-rise buildings. Ambient air was sampled through a heated TSP sampling head (DTSP03/00/16, Digitel, Switzerland) on a 2 m sampling tube. A flow splitter was used to direct part of the air flow into PINE. Each expansion was operated with the following program: (1) Flush for 5 min at 2 L min<sup>−1</sup>, (2) expansion at 3 L min<sup>−1</sup> down to 840 mbar, (3) refill at 2 L min<sup>−1</sup>. A year gap between the two campaigns occurred due to the deployment of the PINE instrument on the M-Phase campaign. The mean volume of air sampled during a single expansion was <inline-formula><mml:math id="M84" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">2.0</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup> for both campaign periods. The datasets are published <xref ref-type="bibr" rid="bib1.bibx69 bib1.bibx70" id="paren.44"/>, but no accompanying papers are currently available.</p>
      <p id="d2e2310"><italic>SBO21</italic>: PINE measurements were made at the Sonnblick Observatory (SBO), which is located in Austria on the summit of the mount “Hoher Sonnblick”. Ambient air was sampled through a heated total aerosol inlet designed according to GAW guidelines, with an upper cut-off size of 20 <inline-formula><mml:math id="M86" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula> at a wind speed of 20 m s<sup>−1</sup>. The inlet was located at the rooftop of the station. Each expansion in PINE was operated with the following program: (1) Flush for 4 min at 1.5 L min<sup>−1</sup>, (2) expansion at 3 L min<sup>−1</sup> down to 575–600 mbar, (3) refill at 1.5 L min<sup>−1</sup>. The mean volume of air sampled during a single expansion was <inline-formula><mml:math id="M91" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">1.4</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup>. More information can be found in <xref ref-type="bibr" rid="bib1.bibx5" id="text.45"/>.</p>
      <p id="d2e2407"><italic>SPL01</italic>: PINE measured INP concentrations at Storm Peak Laboratory, a mountain-top site in the Rocky Mountains <xref ref-type="bibr" rid="bib1.bibx32" id="paren.46"/>, US. The site is operated by the University of Utah and supported by the National Science Foundation (NSF). The instrument was connected to one of the facility’s heated inlets, <xref ref-type="bibr" rid="bib1.bibx61" id="paren.47"/> with the following measurement program: (1) Flush for 5 min at 1 L min<sup>−1</sup>, (2) expansion at 3 L min<sup>−1</sup> to 530 mbar (ambient pressure 685 mbar), (3) refill at 1 L min<sup>−1</sup>. The mean volume of air sampled during a single expansion was <inline-formula><mml:math id="M96" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">1.4</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup>. The dataset is published <xref ref-type="bibr" rid="bib1.bibx50" id="paren.48"/>, but no accompanying paper is currently available.</p>
      <p id="d2e2487"><italic>CALISHTO</italic>: INP measurements were performed at the (HAC)<sup>2</sup> (Helmos Hellenic Atmospheric Aerosol and Climate Change) station at the top of Mount Helmos, Greece. Sampling was performed through an omnidirectional total inlet installed on the rooftop of the observatory. PINE was operated with the following program: (1) Flush for 5 min at 1 L min<sup>−1</sup>, (2) expansion at 3 L min<sup>−1</sup> to 650 mbar, (3) refill at 1 L min<sup>−1</sup>. The mean volume of air sampled during a single expansion was <inline-formula><mml:math id="M102" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">1.5</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup>. Information on the measurement campaign can be found in <xref ref-type="bibr" rid="bib1.bibx29" id="text.49"/>.</p>
      <p id="d2e2570"><italic>ExINP-NSA</italic>: PINE measurements were made at the Barrow Atmospheric Baseline Observatory, next to the US DOE North Slope of Alaska (NSA) ARM site, located close to the town of Utqiaġvik, Alaska. The PINE instrument was located inside the observatory and sampled ambient air through stainless steel sampling pickup inlets (19 mm diameter) connected to a polyvinyl chloride vertical sampling stack (0.1 m diameter) 12 m above the ground. A 9.5 mm conductive tube bridged the pickup port to the instruments in the observatory. The measurement program was the following: (1) Flush for 10 min at 2 L min<sup>−1</sup>, (2) expansion at 3 L min<sup>−1</sup> to 800 mbar, (3) refill at 2 L min<sup>−1</sup> to ambient pressure. The mean volume of air sampled during a single expansion was <inline-formula><mml:math id="M107" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">3.0</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup>. More information can be found in <xref ref-type="bibr" rid="bib1.bibx60" id="text.50"/>.</p>
      <p id="d2e2644"><italic>BEIJING</italic>. INP measurements were made at the Beijing Cloud Laboratory and Observational Utilities Deployment Base (CLOUD Base, CB). The site is located in Pinggu District, Beijing, China, and is situated on a plain surrounded by farmland and low-rise buildings. The instrument was installed at a PM<sub>1</sub> non-heated aerosol inlet as icing was not expected during winter. The inlet was installed 1 m above the laboratory roof and 12 m above ground. The measurement program was the following: (1) Flush for 3 min at 1.5 L min<sup>−1</sup>, (2) expansion at 3 L min<sup>−1</sup> to 850 mbar, (3) refill at 1.5 L min<sup>−1</sup>. Note that measurements below the lower limit of measurable concentration (see Sect. <xref ref-type="sec" rid="Ch1.S3"/>) were not included in the Level 1 data for this campaign; therefore, we omit all data above a temperature threshold of 252.15 K. This removes expansions within a temperature regime where we would expect to encounter low concentrations with poor counting statistics (see Fig. <xref ref-type="fig" rid="F5"/>). The mean volume of air sampled during a single expansion was <inline-formula><mml:math id="M113" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">2.4</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup>. The dataset is published <xref ref-type="bibr" rid="bib1.bibx36" id="paren.51"/>, but no accompanying paper is currently available.</p>
      <p id="d2e2731"><italic>M-Phase</italic>: PINE measurements were made within the Leeds IcePod mobile laboratory <xref ref-type="bibr" rid="bib1.bibx59" id="paren.52"/> onboard the RRS Discovery during the joint M-Phase/SEANA research cruise in the Labrador Sea. The IcePod was located on the foredeck, forward of the ship stack. Ambient air was sampled through a heated TSP sampling head (DTSP03/00/16, Digitel, Switzerland) on a 2 m sampling tube. A flow splitter was used to direct part of the air flow into PINE. Each expansion was operated with the following program: (1) Flush for 5 min at 2 L min<sup>−1</sup>, (2) expansion at 3 L min<sup>−1</sup> down to 840 mbar, (3) refill at 2 L min<sup>−1</sup>. The mean volume of air sampled during a single expansion was <inline-formula><mml:math id="M118" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">2.2</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup>. The dataset is published <xref ref-type="bibr" rid="bib1.bibx71" id="paren.53"/>, but no accompanying paper is currently available.</p>
      <p id="d2e2808"><italic>PaCE22</italic>: PINE measurements were conducted at the Pallas Sammaltunturi station in northern Finland around 170 km north of the Arctic Circle, representing sub-Arctic conditions. The site is within the Pallas-Yllästunturi National Park on top of a hill above the boreal forest tree line. PINE was connected to the heated whole air inlet of the station. Each expansion was operated with the following program: (1) Flush for 4 min at 1 L min<sup>−1</sup>, (2) expansion at 3 L min<sup>−1</sup> from around 950 to 800 mbar, (3) refill at 1 L min<sup>−1</sup>. The mean volume of air sampled during a single expansion was <inline-formula><mml:math id="M123" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">1.8</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup>. An overview of the PaCE22 (Pallas Cloud Experiment 2022) campaign can be found in <xref ref-type="bibr" rid="bib1.bibx16" id="text.54"/>, <xref ref-type="bibr" rid="bib1.bibx12" id="text.55"/>, and <xref ref-type="bibr" rid="bib1.bibx31" id="text.56"/>, whilst more information on the PINE measurements can be found in <xref ref-type="bibr" rid="bib1.bibx11" id="text.57"/>.</p>
      <p id="d2e2892"><italic>PUY</italic>: PINE measurements were made at the Puy de Dôme station, located at the highest point of the Chaîne des Puys, a volcanic mountain range in central France. PINE was connected to the whole air inlet of the station (50 % loss of particles at 30 <inline-formula><mml:math id="M125" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>). Ambient aerosol particles sampled through the inlet are passively dried to approximately 40 % relative humidity with respect to water (RHw) before being sampled by the instrument. Each expansion was operated with the following program: (1) Flush for 7.5 min at 2 L min<sup>−1</sup>, (2) expansion at 3 L min<sup>−1</sup> from around 850 to 700 mbar, (3) refill at 2 L min<sup>−1</sup>. The mean volume of air sampled during a single expansion was <inline-formula><mml:math id="M129" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">1.9</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup>. More information on this campaign can be found in <xref ref-type="bibr" rid="bib1.bibx20" id="text.58"/>.</p>
      <p id="d2e2976"><italic>ExINP-GVB</italic>: INP concentration measurements during this campaign were performed at Gruvebadet Atmosphere Laboratory in the high Arctic, in close proximity of the Ny-Alesund Research Station <xref ref-type="bibr" rid="bib1.bibx55" id="paren.59"/> on Svalbard. PINE was attached to the heated whole air inlet and the following measurement program was used: (1) Flush for 5 min at 2 L min<sup>−1</sup>, (2) expansion at 3 L min<sup>−1</sup> from around 1000 to 850 mbar, (3) refill at 2 L min<sup>−1</sup>. The mean volume of air sampled during a single expansion was <inline-formula><mml:math id="M134" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">2.4</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup>. The dataset is published <xref ref-type="bibr" rid="bib1.bibx8" id="paren.60"/>, but no accompanying paper is currently available.</p>
      <p id="d2e3053"><italic>LIFE-FROSTDEFEND</italic>: INP measurements during the LIFE-FROSTDEFEND campaign were conducted in a mobile container, placed in a lemon orchard near Aigio, Greece. Aerosol sampling was performed via a PM<sub>10</sub> inlet, about 1 m above the container roof. PINE was operated with the following program: (1) Flush for 4 min a 1 L min<sup>−1</sup>, (2) expansion with 3 L min<sup>−1</sup> to 880 mbar, (3) refill at 1 L min<sup>−1</sup>. The mean volume of air sampled during a single expansion was <inline-formula><mml:math id="M140" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">1.8</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup>. The dataset is published <xref ref-type="bibr" rid="bib1.bibx74" id="paren.61"/>, but no accompanying paper is currently available.</p>
      <p id="d2e3136"><italic>ExINP-ZEP</italic>: PINE measurements were performed at the Zeppelin observatory at Mt. Zeppelin <xref ref-type="bibr" rid="bib1.bibx62" id="paren.62"/>, located in the high Arctic on Svalbard. The station is located south of Ny-Alesund Research Station, however, due to frequent temperature inversions the site is typically shielded from anthropogenic emission from the village. PINE was connected to a ground-based counterflow virtual impactor for measurements of cloud residuals, and a heated whole air inlet (see <xref ref-type="bibr" rid="bib1.bibx47" id="altparen.63"/>, for more details). In this study we only present measurements from the whole air inlet. The following program was used: (1) Flush for 5 min at 2 L min<sup>−1</sup>, (2) expansion at 3 L min<sup>−1</sup> from around 930 to 790 mbar, (3) refill at 2 L min<sup>−1</sup>. The mean volume of air sampled during a single expansion was <inline-formula><mml:math id="M145" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">2.1</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup>. The dataset is published <xref ref-type="bibr" rid="bib1.bibx9" id="paren.64"/>, but no accompanying paper is currently available.</p>
      <p id="d2e3216"><italic>WAO</italic>: PINE measurements were made within the Leeds IcePod mobile laboratory <xref ref-type="bibr" rid="bib1.bibx59" id="paren.65"/> located at the Weybourne Atmospheric Observatory (WAO), a GAW Regional station on the Norfolk coast of the UK. Ambient air was sampled through a heated TSP sampling head (DTSP03/00/16, Digitel, Switzerland) on a 2 m sampling tube. A flow splitter was used to direct part of the air flow into PINE. Each expansion was operated with the following program: (1) Flush for 5 min at 2 L min<sup>−1</sup>, (2) expansion at 3 L min<sup>−1</sup> down to 840 mbar, (3) refill at 2 L min<sup>−1</sup>. The mean volume of air sampled during a single expansion was <inline-formula><mml:math id="M150" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">2.5</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> m<sup>3</sup>. The dataset is published <xref ref-type="bibr" rid="bib1.bibx36" id="paren.66"/>, but no accompanying paper is currently available.</p>
</sec>
<sec id="Ch1.S2.SS3">
  <label>2.3</label><title>PINE analysis software</title>
      <p id="d2e3301">Post-processing software is used to convert the raw Level 0 data from each PINE run into relevant Level 1 data; a full description and evaluation can be found in <xref ref-type="bibr" rid="bib1.bibx18" id="text.67"/>. The key information taken from the Level 0 data includes the minimum temperature during the expansion and the hydrometeor size distribution measured by the OPC between the start and end of the expansion, which the software uses to establish a size threshold for each expansion. This threshold, typically on the order of 10 <inline-formula><mml:math id="M152" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula> <xref ref-type="bibr" rid="bib1.bibx56" id="paren.68"/>, is used to separate the smaller liquid droplets from the larger ice particles, thus determining <inline-formula><mml:math id="M153" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">ice</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>; <inline-formula><mml:math id="M154" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> can then be calculated following the process described in Sect. <xref ref-type="sec" rid="Ch1.S2.SS1"/>. The key Level 1 variables that are relevant for this dataset are the expansion time (UTC), the minimum temperature (<inline-formula><mml:math id="M155" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">min</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>), and the number concentration of activated INPs per liter of sampled air under standard conditions (273.15 K and 1013.25 hPa). Seventeen of the campaign datasets (Table <xref ref-type="table" rid="T1"/>) use the publicly available PINE INP Analysis software <xref ref-type="bibr" rid="bib1.bibx17" id="paren.69"><named-content content-type="pre">PIA</named-content></xref>, described by <xref ref-type="bibr" rid="bib1.bibx18" id="text.70"/> and developed at KIT, and three use a variation developed by WTAMU, which differs in how the size threshold is determined. In the PIA software, the threshold is determined for each PINE expansion (see <xref ref-type="bibr" rid="bib1.bibx18" id="altparen.71"/>, for an evaluation of this method), and in the WTAMU software, a single threshold is determined over an “operation period” which may include anywhere between one run and more than 100 runs <xref ref-type="bibr" rid="bib1.bibx80" id="paren.72"><named-content content-type="post">Sect. S8</named-content></xref>. Both software packages include automatic quality control to flag the data, which is used by each campaign team to remove poor quality data. A comparison of the two methods for the ExINP-ENA Level 1 data yields an <inline-formula><mml:math id="M156" display="inline"><mml:mi>r</mml:mi></mml:math></inline-formula>-squared value of 0.97, demonstrating good consistency between the methods. For some campaigns, upper or lower temperature ranges were used to manually flag data from temperature regimes that are deemed uncertain. In this dataset, we have removed all Level 1 flagged data.</p>

      <fig id="F2" specific-use="star"><label>Figure 2</label><caption><p id="d2e3384">Time series of INP measurements from the ExINP-GVB campaign demonstrating the subsetting method. Panel <bold>(a)</bold> shows the Level 1 <inline-formula><mml:math id="M157" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> time series as a function of temperature (colors), with each marker showing the measurements from a single PINE expansion. Concentrations are per standard liter of measured air at 273.15 K and 1013.25 hPa. Expansions where <inline-formula><mml:math id="M158" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is below <inline-formula><mml:math id="M159" display="inline"><mml:mrow><mml:munder><mml:mi mathvariant="normal">lim</mml:mi><mml:mo mathvariant="normal">¯</mml:mo></mml:munder><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> (shown as the grey dashed line) are shown at the bottom of the panel. Panel <bold>(b)</bold> shows <inline-formula><mml:math id="M160" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> averaged over intervals of 6 h and 2 K and includes <inline-formula><mml:math id="M161" display="inline"><mml:mrow><mml:munder><mml:mi mathvariant="normal">lim</mml:mi><mml:mo mathvariant="normal">¯</mml:mo></mml:munder><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> from the Level 1 data. Intervals where <inline-formula><mml:math id="M162" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0</mml:mn></mml:mrow></mml:math></inline-formula> are shown at the bottom of the panel. Error bars due to the uncertainty in the OPC (see Sect. <xref ref-type="sec" rid="Ch1.S2.SS1"/>) are included on all data points but are too small to distinguish from the symbol.</p></caption>
          <graphic xlink:href="https://essd.copernicus.org/articles/18/6465/2026/essd-18-6465-2026-f02.png"/>

        </fig>

</sec>
</sec>
<sec id="Ch1.S3">
  <label>3</label><title>Data subsetting</title>
      <p id="d2e3511">In our dataset, we subset the time series Level 1 data from each campaign (e.g., Fig. <xref ref-type="fig" rid="F2"/>a) into regular 6 h intervals (daily 00:00, 06:00, 12:00, 18:00, 24:00 UTC) and 2 K temperature bins (e.g., Fig. <xref ref-type="fig" rid="F2"/>b). We provide time series of the mean measured INP concentration from the intervals (<inline-formula><mml:math id="M163" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>, where <inline-formula><mml:math id="M164" display="inline"><mml:mi>T</mml:mi></mml:math></inline-formula> and <inline-formula><mml:math id="M165" display="inline"><mml:mi>t</mml:mi></mml:math></inline-formula> denote the temperature and time interval) and the associated mean minimum temperature measured during the subset of PINE expansions (<inline-formula><mml:math id="M166" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">min</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>). We averaged the data for two reasons. The lowest measurable <inline-formula><mml:math id="M167" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">ice</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> in PINE is implicitly one ice crystal (<inline-formula><mml:math id="M168" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">ice</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:math></inline-formula>) per sampled volume; this corresponds to a lower limit of the measurable INP concentration (<inline-formula><mml:math id="M169" display="inline"><mml:mrow><mml:munder><mml:mi mathvariant="normal">lim</mml:mi><mml:mo mathvariant="normal">¯</mml:mo></mml:munder><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>) that is a function of the instrument configuration and ambient conditions. In order to improve counting statistics and reduce <inline-formula><mml:math id="M170" display="inline"><mml:mrow><mml:munder><mml:mi mathvariant="normal">lim</mml:mi><mml:mo mathvariant="normal">¯</mml:mo></mml:munder><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, we use data collected from multiple expansions to increase the total volume of air that is considered and the statistical likelihood of measuring at least one ice crystal in this volume. This likelihood is inversely scaled with the number of expansions (and therefore total sample volume) that are included in the interval, as demonstrated in Fig. <xref ref-type="fig" rid="F2"/>. In this example, the Level 1 data for the ExINP-GVB campaign has a lower measurable concentration of <inline-formula><mml:math id="M171" display="inline"><mml:mrow><mml:munder><mml:mi mathvariant="normal">lim</mml:mi><mml:mo mathvariant="normal">¯</mml:mo></mml:munder><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>≈</mml:mo><mml:mn mathvariant="normal">0.5</mml:mn></mml:mrow></mml:math></inline-formula> L<sup>−1</sup> (Fig. <xref ref-type="fig" rid="F2"/>a) and includes many expansions below this value (shown as zero counts), whilst the lowest concentration from the subset data, <inline-formula><mml:math id="M173" display="inline"><mml:mrow><mml:munder><mml:mi mathvariant="normal">lim</mml:mi><mml:mo mathvariant="normal">¯</mml:mo></mml:munder><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> (Fig. <xref ref-type="fig" rid="F2"/>b), is now regularly lower than the Level 1 limit, providing additional data primarily at higher freezing temperatures where INPs are rarer. The number of zero counts has been reduced from 639 in the Level 1 data to 10 in the subset data, which demonstrates how the zero counts from individual expansions can still provide useful information. Subsetting into regular time intervals also makes the dataset easy to apply to simulation studies, meteorological reanalysis products, and back-trajectory analyses. Global simulation studies generally restrict the frequency of output to synoptic timescales, which will range from hours to days. Similarly, reanalysis products such as ERA5 <xref ref-type="bibr" rid="bib1.bibx37" id="paren.73"/> and MERRA2 <xref ref-type="bibr" rid="bib1.bibx30" id="paren.74"/> are available at regular intervals. Back-trajectory analysis packages, such as HYSPLIT <xref ref-type="bibr" rid="bib1.bibx66" id="paren.75"/> and FLEXPART <xref ref-type="bibr" rid="bib1.bibx2" id="paren.76"/> can be driven by reanalysis products. A time period of 6 h was chosen to maximize the number of expansions within the interval, whilst maintaining a time scale that is appropriate for synoptic scale meteorology and the applications discussed above. The subsetting method applied to the Level 1 data from the 20 campaigns takes <inline-formula><mml:math id="M174" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> measurements from 423 678 expansions (367 487 at or above <inline-formula><mml:math id="M175" display="inline"><mml:mrow><mml:munder><mml:mi mathvariant="normal">lim</mml:mi><mml:mo mathvariant="normal">¯</mml:mo></mml:munder><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>) and results in 35 960 non-zero <inline-formula><mml:math id="M176" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> measurements in the dataset.</p>
      <p id="d2e3759">Some portions of the data sets suffer from poor counting statistics; hence, for each collection of subset data, we determine an associated error based on counting statistics, which we calculate as the relative standard deviation (RSD) of the data within each interval using the equation <inline-formula><mml:math id="M177" display="inline"><mml:mrow><mml:mi mathvariant="normal">RSD</mml:mi><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:msub><mml:mi mathvariant="italic">σ</mml:mi><mml:mi>N</mml:mi></mml:msub><mml:msub><mml:mi/><mml:mi mathvariant="normal">ice</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>/</mml:mo><mml:msub><mml:mi mathvariant="italic">μ</mml:mi><mml:mi>N</mml:mi></mml:msub><mml:msub><mml:mi/><mml:mi mathvariant="normal">ice</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>, where <inline-formula><mml:math id="M178" display="inline"><mml:mi mathvariant="italic">σ</mml:mi></mml:math></inline-formula> and <inline-formula><mml:math id="M179" display="inline"><mml:mi mathvariant="italic">μ</mml:mi></mml:math></inline-formula> are the standard deviation and mean of <inline-formula><mml:math id="M180" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">ice</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>, the total number of ice crystals measured in the interval. <inline-formula><mml:math id="M181" display="inline"><mml:mrow><mml:mi mathvariant="normal">RSD</mml:mi><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> is a statistical measure of how robust the data point is, given the total number of events (ice crystals in our case) that have been measured. The ice crystal detections will follow a Poisson distribution; therefore, the equation can be rewritten as <inline-formula><mml:math id="M182" display="inline"><mml:mrow><mml:mi mathvariant="normal">RSD</mml:mi><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:mn mathvariant="normal">1</mml:mn><mml:mo>/</mml:mo><mml:msqrt><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">ice</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:msqrt><mml:mo>×</mml:mo><mml:mn mathvariant="normal">100</mml:mn></mml:mrow></mml:math></inline-formula> and is expressed as a percentage. <inline-formula><mml:math id="M183" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">ice</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> is calculated by summing <inline-formula><mml:math id="M184" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">ice</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> from all expansions within the interval, which can be calculated with the assumption that <inline-formula><mml:math id="M185" display="inline"><mml:mrow><mml:munder><mml:mi mathvariant="normal">lim</mml:mi><mml:mo mathvariant="normal">¯</mml:mo></mml:munder><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> from the Level 1 data is the concentration that corresponds to <inline-formula><mml:math id="M186" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">ice</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:math></inline-formula> being measured in a single PINE expansion; dividing <inline-formula><mml:math id="M187" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> by this value results in <inline-formula><mml:math id="M188" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">ice</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>. <inline-formula><mml:math id="M189" display="inline"><mml:mrow><mml:munder><mml:mi mathvariant="normal">lim</mml:mi><mml:mo mathvariant="normal">¯</mml:mo></mml:munder><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is a function of the PINE instrument setup and ambient conditions and may vary during the campaign duration (note the small variations around the mean <inline-formula><mml:math id="M190" display="inline"><mml:mrow><mml:munder><mml:mi mathvariant="normal">lim</mml:mi><mml:mo mathvariant="normal">¯</mml:mo></mml:munder><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> in Fig. <xref ref-type="fig" rid="F2"/>a); therefore, a 3 d moving time window is applied to the Level 1 data to allow small variations throughout the time series of each campaign. The value of <inline-formula><mml:math id="M191" display="inline"><mml:mrow><mml:mi mathvariant="normal">RSD</mml:mi><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> can be used along with a threshold value to statistically remove uncertain data. Figure <xref ref-type="fig" rid="F3"/> demonstrates the sensitivity of the threshold on the percentage of subset data that would be removed from each campaign. The value of this threshold is left to the user of GloPINE.</p>

      <fig id="F3" specific-use="star"><label>Figure 3</label><caption><p id="d2e4065">Figure demonstrating the application of different threshold <inline-formula><mml:math id="M192" display="inline"><mml:mrow><mml:mi mathvariant="normal">RSD</mml:mi><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> values (20 %, 30 %, 40 %, and 50 %) to the subset <inline-formula><mml:math id="M193" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> measurements in each campaign. Each threshold <inline-formula><mml:math id="M194" display="inline"><mml:mrow><mml:mi mathvariant="normal">RSD</mml:mi><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> value has a corresponding threshold of <inline-formula><mml:math id="M195" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">ice</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> shown in the legend.</p></caption>
        <graphic xlink:href="https://essd.copernicus.org/articles/18/6465/2026/essd-18-6465-2026-f03.png"/>

      </fig>

</sec>
<sec id="Ch1.S4">
  <label>4</label><title>The GloPINE INP dataset</title>
      <p id="d2e4160">In this section, we present an overview of the new GloPINE INP dataset (without application of a <inline-formula><mml:math id="M196" display="inline"><mml:mrow><mml:mi mathvariant="normal">RSD</mml:mi><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> threshold). Figure <xref ref-type="fig" rid="F4"/> shows the distributions of <inline-formula><mml:math id="M197" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">min</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> for each PINE campaign in the dataset. There is good coverage of freezing temperatures, ranging from 240 to 263 K. Some campaigns, such as BEIJING and ExINP-SGP, have a consistent and wide-ranging distribution of <inline-formula><mml:math id="M198" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">min</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>, while others, such as M-Phase and CALISHTO, have measurements weighted towards a smaller range of <inline-formula><mml:math id="M199" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">min</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>. This reflects the sampling method chosen by each team. Keeping the temperature relatively constant (see the initial period in Fig. <xref ref-type="fig" rid="F2"/>a) provides a higher number of expansions to combine in the time interval, which increases the sensitivity of the measurements (see Sect. <xref ref-type="sec" rid="Ch1.S3"/>). This is a good method for measuring <inline-formula><mml:math id="M200" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> close to <inline-formula><mml:math id="M201" display="inline"><mml:mrow><mml:munder><mml:mi mathvariant="normal">lim</mml:mi><mml:mo mathvariant="normal">¯</mml:mo></mml:munder><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>. However, cycling through different temperatures (see the latter period in Fig. <xref ref-type="fig" rid="F2"/>a) provides a more complete picture of the INP spectrum, although without the increase in sensitivity. This method may be particularly advantageous when INP sources or concentrations are likely to vary on short timescales, such as in dust or biomass burning plumes. Figure <xref ref-type="fig" rid="F4"/> also shows the number of <inline-formula><mml:math id="M202" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> measurements from each campaign within the INP dataset, which totals 35 960 non-zero measurements. For comparison, <xref ref-type="bibr" rid="bib1.bibx35" id="text.77"/> and <xref ref-type="bibr" rid="bib1.bibx22" id="text.78"/> recently evaluated their global INP models with datasets that include 1000s of immersion-mode INP measurements.</p>

      <fig id="F4" specific-use="star"><label>Figure 4</label><caption><p id="d2e4312">Normalized frequency distribution of temperatures from the 6 h subset data for each campaign. The number of non-zero <inline-formula><mml:math id="M203" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> measurements from each campaign in the dataset (<inline-formula><mml:math id="M204" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>) is shown in the top right corner of each plot. All campaigns use the same range on the <inline-formula><mml:math id="M205" display="inline"><mml:mi>x</mml:mi></mml:math></inline-formula>-axis except for HyICE2018.</p></caption>
        <graphic xlink:href="https://essd.copernicus.org/articles/18/6465/2026/essd-18-6465-2026-f04.png"/>

      </fig>

      <p id="d2e4367">Figure <xref ref-type="fig" rid="F5"/> shows the distributions and medians of non-zero <inline-formula><mml:math id="M206" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> as a function of temperature and campaign. <inline-formula><mml:math id="M207" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> measurements span a range from less than 0.01 L<sup>−1</sup> to over 1000 L<sup>−1</sup> over a temperature range from 240 to 263 K. Higher concentrations are observed at lower temperatures for all campaigns, and there is considerable variability between each location, with medians spanning two orders of magnitude at most temperatures. The highest <inline-formula><mml:math id="M210" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> measurements were observed in the BEIJING campaign, and the lowest are commonly observed in high-latitude environments, such as PaCE22 (northern Finland) and ExINP-ZEP (Svalbard). The PINE INP dataset provides a diverse range of <inline-formula><mml:math id="M211" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> measurements and is consistent with the spatial variability of INP sources and concentrations within the Northern Hemisphere <xref ref-type="bibr" rid="bib1.bibx46" id="paren.79"><named-content content-type="pre">e.g.,</named-content></xref>.</p>

      <fig id="F5" specific-use="star"><label>Figure 5</label><caption><p id="d2e4489">Distribution of non-zero <inline-formula><mml:math id="M212" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> measurements as a function of <inline-formula><mml:math id="M213" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">min</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> from the 6 h subset data for each campaign in the GloPINE dataset. Concentrations are per standard liter of sampled air at 273.15 K and 1013.25 hPa. The distributions are presented as box plots: the black line shows the median, the bar shows the inter-quartile range, and the whiskers extend to the farthest data point lying within 150 % of the inter-quartile range. The integers in red shown below each box plot are the percentage of GloPINE data where <inline-formula><mml:math id="M214" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>&lt;</mml:mo><mml:munder><mml:mi mathvariant="normal">lim</mml:mi><mml:mo mathvariant="normal">¯</mml:mo></mml:munder><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>. The larger plot on the right shows the median values of <inline-formula><mml:math id="M215" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> as a function of <inline-formula><mml:math id="M216" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">min</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>. The different colored symbols correspond to each campaign; symbols are offset in each temperature bin for clarity.</p></caption>
        <graphic xlink:href="https://essd.copernicus.org/articles/18/6465/2026/essd-18-6465-2026-f05.png"/>

      </fig>

<sec id="Ch1.S4.SS1">
  <label>4.1</label><title>GloPINE dataset format</title>
      <p id="d2e4633">The GloPINE dataset <xref ref-type="bibr" rid="bib1.bibx36" id="paren.80"/> is provided as individual NetCDF4 (.nc) files corresponding to each campaign. This permits the addition of further campaigns in the future. The file attributes are the campaign name, the PINE instrument used to make the measurements, and the duration of the campaign. Each variable is suffixed with “6h” or “6h2K” to denote the subsetting interval in time or time/temperature. The variables included in each file are:</p>
      <p id="d2e4639"><list list-type="bullet">
            <list-item>

      <p id="d2e4644"><italic>latitude_6h</italic>: mean latitude(s) of campaign (° N)</p>
            </list-item>
            <list-item>

      <p id="d2e4652"><italic>longitude_6h</italic>: mean longitude(s) of campaign (° E)</p>
            </list-item>
            <list-item>

      <p id="d2e4660"><italic>altitude_6h</italic>: mean altitude(s) of PINE inlet (m above sea level)</p>
            </list-item>
            <list-item>

      <p id="d2e4668"><italic>time_6h</italic>: mean time of INP measurements in interval (fractional days since 1900-01-01)</p>
            </list-item>
            <list-item>

      <p id="d2e4676"><italic>INP_6h2K</italic>: mean INP concentration in interval (m<sup>−3</sup>)</p>
            </list-item>
            <list-item>

      <p id="d2e4697"><italic>INP_uncertainty_6h2K</italic>: mean INP concentration uncertainty in interval (<inline-formula><mml:math id="M218" display="inline"><mml:mo lspace="0mm">±</mml:mo></mml:math></inline-formula> m<sup>−3</sup>)</p>
            </list-item>
            <list-item>

      <p id="d2e4724"><italic>temperature_6h2K</italic>: mean PINE minimum temperature in interval (K)</p>
            </list-item>
            <list-item>

      <p id="d2e4732"><italic>Vtot_6h2K</italic>: total volume of ambient air sampled in interval (m<sup>3</sup>)</p>
            </list-item>
            <list-item>

      <p id="d2e4749"><italic>RSD_6h2K</italic>: relative standard deviation of <inline-formula><mml:math id="M221" display="inline"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi mathvariant="normal">ice</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> in interval, expressed as a percentage (%)</p>
            </list-item>
          </list></p>
      <p id="d2e4767">Note that concentrations of <inline-formula><mml:math id="M222" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> are converted from units of L<sup>−1</sup> and provided in SI units of m<sup>−3</sup>. Missing values are set to a value of <inline-formula><mml:math id="M225" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>999 and represent binning intervals that did not include any PINE expansions. Values of zero in the variables <italic>INP_6h2K</italic> and <italic>RSD_6h2K</italic> represent intervals when at least one PINE expansion was performed but the sum of measured ice crystals was zero; we keep these in for consistency with the Level 1 datasets. All campaigns have geospatial variables (<italic>latitude_6h</italic>, <italic>longitude_6h</italic>, and <italic>altitude_6h</italic>) in dimensions of time. This permits a consistent format for campaigns that were either in a fixed location (e.g., ExINP-NSA) or a variable location (e.g., the M-Phase cruise campaign). For campaigns in a fixed location, the values are constant throughout the timeseries. For application of the GloPINE dataset with simulated data or reanalysis products, we recommend that the user collocates their data with the latitude, longitude, time, and altitude of each individual GloPINE data point. When calculating INP concentrations, we recommend applying the interval-mean temperature and, for consistency with the measurement technique, only using the portion of the aerosol size distribution below <inline-formula><mml:math id="M226" display="inline"><mml:mrow><mml:mi>D</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">5</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M227" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula> (see Sect. <xref ref-type="sec" rid="Ch1.S2.SS1"/>).</p>

<table-wrap id="T2" specific-use="star"><label>Table 2</label><caption><p id="d2e4867">Location of Level 1 data from each PINE measurement campaign collated in this dataset.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="3">
     <oasis:colspec colnum="1" colname="col1" align="justify" colwidth="3cm"/>
     <oasis:colspec colnum="2" colname="col2" align="justify" colwidth="7cm"/>
     <oasis:colspec colnum="3" colname="col3" align="justify" colwidth="4cm"/>
     <oasis:thead>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Name of campaign</oasis:entry>
         <oasis:entry colname="col2">Access location of Level 1 dataset</oasis:entry>
         <oasis:entry colname="col3">Reference</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">HyICE2018</oasis:entry>
         <oasis:entry colname="col2"><uri>https://doi.org/10.5281/zenodo.10469663</uri></oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx14" id="text.81"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">PICNIC</oasis:entry>
         <oasis:entry colname="col2"><uri>https://doi.org/10.35097/7gt99xqnh6k8sw42</uri></oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx51" id="text.82"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">ExINP-SGP</oasis:entry>
         <oasis:entry colname="col2">Access via <uri>http://www.arm.gov/research/campaigns/sgp2019exinpsgp</uri> (last access: 13 March 2024)</oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx38" id="text.83"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">ExINP-ENA</oasis:entry>
         <oasis:entry colname="col2">Access via <uri>http://www.arm.gov/research/campaigns/ena2020exinpena</uri> (last access: 13 March 2024)</oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx39" id="text.84"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">CORONA-PINE04</oasis:entry>
         <oasis:entry colname="col2"><uri>https://doi.org/10.35097/j823gv823jbwy7b0</uri></oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx73" id="text.85"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">CORONA-PINE01</oasis:entry>
         <oasis:entry colname="col2"><uri>https://doi.org/10.35097/m61r3c6tn0t3qk1y</uri></oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx49" id="text.86"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">CountIce-pt1</oasis:entry>
         <oasis:entry colname="col2"><uri>https://doi.org/10.5281/zenodo.17451020</uri></oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx69" id="text.87"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">SBO21</oasis:entry>
         <oasis:entry colname="col2"><uri>https://doi.org/10.35097/g828m8z2det94691</uri>, <uri>https://doi.org/10.35097/dxcsmnvfunkx76vx</uri></oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx6 bib1.bibx7" id="text.88"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">SPL01</oasis:entry>
         <oasis:entry colname="col2"><uri>https://doi.org/10.35097/rmfxfbr6jegua5cv</uri></oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx50" id="text.89"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">CALISHTO</oasis:entry>
         <oasis:entry colname="col2"><uri>https://doi.org/10.35097/jxwbwycbyu64n2p0</uri>, <uri>https://doi.org/10.35097/4xawjaqwteyvx4fr</uri></oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx75 bib1.bibx76" id="text.90"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">ExINP-NSA</oasis:entry>
         <oasis:entry colname="col2">Access via <uri>http://www.arm.gov/research/campaigns/nsa2021exinpnsa</uri> (last access: 8 January 2024)</oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx40" id="text.91"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">BEIJING</oasis:entry>
         <oasis:entry colname="col2"><uri>https://doi.org/10.5281/zenodo.16745514</uri></oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx36" id="text.92"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">M-Phase</oasis:entry>
         <oasis:entry colname="col2"><uri>https://doi.org/10.5281/zenodo.17378346</uri></oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx71" id="text.93"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">PaCE22</oasis:entry>
         <oasis:entry colname="col2"><uri>https://doi.org/10.5281/zenodo.13889647</uri></oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx10" id="text.94"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">PUY</oasis:entry>
         <oasis:entry colname="col2"><uri>https://doi.org/10.25519/SH8R-F866</uri></oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx19" id="text.95"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">CountIce-pt2</oasis:entry>
         <oasis:entry colname="col2"><uri>https://doi.org/10.5281/zenodo.17451024</uri></oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx70" id="text.96"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">ExINP-GVB</oasis:entry>
         <oasis:entry colname="col2"><uri>https://doi.org/10.35097/dc0vhg052pzx95wx</uri></oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx8" id="text.97"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">LIFE-FROSTDEFEND</oasis:entry>
         <oasis:entry colname="col2"><uri>https://doi.org/10.35097/mdxcqd7p646cwc0u</uri></oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx74" id="text.98"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">ExINP-ZEP</oasis:entry>
         <oasis:entry colname="col2"><uri>https://doi.org/10.35097/tdwrwzd5z7nakajv</uri></oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx9" id="text.99"/>
                    </oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">WAO</oasis:entry>
         <oasis:entry colname="col2"><uri>https://doi.org/10.5281/zenodo.16745514</uri></oasis:entry>
         <oasis:entry colname="col3">
                      <xref ref-type="bibr" rid="bib1.bibx36" id="text.100"/>
                    </oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

</sec>
</sec>
<sec id="Ch1.S5">
  <label>5</label><title>Code availability</title>
      <p id="d2e5244">The code used to take Level 1 data and subset into the Level 2 data is provided as a python script in the dataset repository <ext-link xlink:href="https://doi.org/10.5281/zenodo.16745514" ext-link-type="DOI">10.5281/zenodo.16745514</ext-link> <xref ref-type="bibr" rid="bib1.bibx36" id="paren.101"/>. We also include a python script that provides examples of how to extract and use the Level 2 data.</p>
</sec>
<sec id="Ch1.S6">
  <label>6</label><title>Data availability</title>
      <p id="d2e5261">The GloPINE INP dataset is publicly available at Zenodo under <ext-link xlink:href="https://doi.org/10.5281/zenodo.16745514" ext-link-type="DOI">10.5281/zenodo.16745514</ext-link> <xref ref-type="bibr" rid="bib1.bibx36" id="paren.102"/>. The repository contains the data as described in Sect. <xref ref-type="sec" rid="Ch1.S4.SS1"/> with a summary overview of the dataset. We plan to continue building on this dataset by including new PINE measurement datasets when they become available. Table <xref ref-type="table" rid="T2"/> provides access details to the Level 1 datasets collated in this study.</p>
</sec>
<sec id="Ch1.S7" sec-type="conclusions">
  <label>7</label><title>Conclusions</title>
      <p id="d2e5282">Aerosols can influence the properties and evolution of mixed phase clouds by acting as INPs that facilitate the freezing of cloud droplets at supercooled temperatures as low as <inline-formula><mml:math id="M228" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">235</mml:mn></mml:mrow></mml:math></inline-formula> K (<inline-formula><mml:math id="M229" display="inline"><mml:mo lspace="0mm">-</mml:mo></mml:math></inline-formula>38 °C). INP measurements made under ambient conditions are necessary to constrain the representation of INPs and primary ice production in models and quantify the role of INPs in the climate system.</p>
      <p id="d2e5302">In this paper, we present GloPINE, a new INP measurement dataset that includes measurements made during 20 campaigns in the Northern Hemisphere, with plans to incorporate Southern Hemisphere campaigns in the future. All campaigns used PINE instruments to autonomously sample ambient air every 5 to 15 min over long periods of time (months to years) and over a wide range of temperatures (239 to 259 K). The collated PINE Level 1 data include measurements from <inline-formula><mml:math id="M230" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">400</mml:mn><mml:mspace linebreak="nobreak" width="0.125em"/><mml:mn mathvariant="normal">000</mml:mn></mml:mrow></mml:math></inline-formula> expansions sampled over <inline-formula><mml:math id="M231" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">70</mml:mn><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mn mathvariant="normal">000</mml:mn></mml:mrow></mml:math></inline-formula> h of operation. In the GloPINE dataset, we average the Level 1 PINE data into 6 h time intervals and 2 K temperature intervals, resulting in <inline-formula><mml:math id="M232" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">36</mml:mn><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mn mathvariant="normal">000</mml:mn></mml:mrow></mml:math></inline-formula> INP measurements sampled from over 800 m<sup>3</sup> of ambient air. Combining expansions within the intervals enhances the counting statistics and decreases the lower limit of measurable INP concentrations, primarily enhancing the number of non-zero INP measurements at relatively high freezing temperatures. All measurements include a the relative standard deviation (RSD) of the data within each interval, based on counting statistics, which describes the associated uncertainty of the INP concentration. The GloPINE INP dataset is designed to be easily applied to modeling studies and other research that requires measurements made at regular time intervals, providing a means to robustly evaluate and constrain global models on a scale that has not previously been possible.</p>
      <p id="d2e5353">A particular strength of this method is the use of a single instrument design and methodology, which removes the uncertainty that is usually introduced when combining measurements from different instruments and techniques. However, we note that there is inherent variability in the instrument versions (Sect. <xref ref-type="sec" rid="Ch1.S2.SS1"/>), the operation method for each campaign (Sect. <xref ref-type="sec" rid="Ch1.S2.SS2"/>), and the post-processing software used to produce the Level 1 data. Nonetheless, this is very likely to introduce considerably less uncertainty than between distinct instruments and methods. An important caveat to this rationale is that the dataset presented here is sensitive to instrument deficiencies associated with PINE that may introduce systematic biases. <xref ref-type="bibr" rid="bib1.bibx64" id="text.103"/> have recently shown that small temperature uncertainties can result in large discrepancies in the reported <inline-formula><mml:math id="M234" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi mathvariant="normal">INP</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> values. The authors identified the inhomogeneous distribution of temperature within the PINE chamber during expansion as a potentially important source of uncertainty. We aim to continue to build the PINE campaign database, and as such there is the possibility of updating previous entries in-line with new understanding or changes to the PIA software that act to reduce biases that may be discovered.</p>
      <p id="d2e5380">The GloPINE dataset is complementary to other INPs datasets, and together they will be important for fully characterizing the role of aerosols as INPs across the entire temperature spectrum. Alternative INP measurement techniques, such as those using offline filter-based sampling methods (e.g., <xref ref-type="bibr" rid="bib1.bibx23" id="altparen.104"/>), often report INP concentration measurements at higher temperatures (typically above 255 K) than the bulk of the GloPINE data in its current iteration. This is achieved through sampling over longer time periods (typically several hours to days), resulting in a relatively coarse temporal-resolution that may fail to capture diurnal cycles in INP availability. Filter samples also provide an opportunity to assess aerosol composition, size distributions, and source attribution using additional analytical techniques. Current technical developments aim to couple PINE with aerosol characterization instruments, thereby complementing online INP measurements with simultaneous, high temporal-resolution information on the sampled aerosol <xref ref-type="bibr" rid="bib1.bibx53" id="paren.105"/>. In summary, we recommend combining the GloPINE dataset with other INP datasets to enable robust model evaluation, improve source attribution, and inform parameterizations across the wider temperature range over which aerosols influence primary ice production.</p>
</sec>

      
      </body>
    <back><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d2e5394">RJH, BJM, OM, NH, LL, AB, MDT, AC, AP, EF, KH, PB, CP and PT came up with the conceptual design of this dataset. LL, AB, MDT, AC, AP, EF, KH, PB, CP, PT, MA, SB, DB, NB, MD, KD, KE, GF, RF, DGG, MIG, AGH, RK, EL, MM, IBM, AN, TK, JR, FV, PZ, NH, OM and BJM were involved in facilitating, obtaining, or analyzing the PINE data. RJH collated the datasets, wrote the code to subset the PINE data, and produced the GloPINE INP dataset. RJH prepared the original draft of the manuscript. All co-authors were involved in reviewing and editing the manuscript.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d2e5400">The contact author has declared that none of the authors has any competing interests.</p>
  </notes><notes notes-type="disclaimer"><title>Disclaimer</title>

      <p id="d2e5406">Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. The authors bear the ultimate responsibility for providing appropriate place names. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.</p>
  </notes><ack><title>Acknowledgements</title><p id="d2e5412">RJH acknowledges JASMIN (<uri>http://jasmin.ac.uk/</uri>, last access: 7 August 2025), the UK collaborative data analysis facility. The PUY authors wish to acknowledge CNRS-INSU for supporting measurements performed at the SI-COPDD, and those within the long-term monitoring aerosol program SNO-CLAP, both of which are components of the ACTRIS French Research Infrastructure, and whose data is hosted at the AERIS data center (<uri>https://www.aeris-data.fr/</uri>, last access: 1 September 2026). NH acknowledges support by the US Department of Energy, Office of Science, Office of Biological and Environmental Research (grant no. DE-SC-0018979). The PUY campaign was supported by the National Research Agency under the JCJC program “ANR-21-CE01-0003”, and under the France 2030 program (Obs4Clim) “ANR-21-ESRE-0013”. AC is funded by the ACME project (ANR-21-CE01-0003). PaCE22 was supported by ACTRIS-Finland funding through the Ministry of Transport and Communications, the Atmosphere and Climate Competence Center Flagship funding by the Research Council of Finland (Grant 337552). This project has also received funding from the European Union, H2020 research and innovation program (ACTRIS-IMP, the European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases, Grant 871115). KE, MIG and DGG acknowledge grant LIFE20 CCA/GR/001747 for supporting the LIFE-FROSTDEFEND project. OM acknowledges financial support through the KIT Technology Transfer project PINE (project number N059). LL acknowledges the Postdoctoral Researchers International Mobility Experience (PRIME) program funded by the German Academic Exchange Service (DAAD) with funds from the German Federal Ministry of Education and Research (BMBF) under project number 57572852. The SPL01 campaign and AGH acknowledge the support of Daniel Cziczo and the United States National Science Foundation, Directorate for Geosciences (grants 1749851 and 2054847). NH and MM thank the National Research Council of Italy and the staff of the Arctic Station Dirigibile Italia for their assistance during the ExINP-GVB campaigns. M-Phase acknowledges the National Oceanography Center (NOC), the captain and crew of the RRS Discovery, and funding support under the NERC grants NE/T00648X/1 (M-Phase) and NE/S00579X/1 (SEANA, that funded the ship time). TP acknowledges support of Zoe Brasseur, Jonathan Duplissy and the SMEAR II staff during HyICE2018. TP also acknowledges support of Research Council of Finland through Atmosphere and Climate Competence Center (ACCC) and financial support of University of Helsinki (ACTRIS-HY). The HyICE2018 campaign was supported via transnational access project through ACTRIS-2. WAO acknowledges funding support under the NERC grant NE/T006420/1 (DCMEX) and would like to acknowledge the Atmospheric Measurement and Observation Facility (AMOF), a Natural Environment Research Council (UKRI-NERC) funded facility (NE/Y005376/1), for providing access to the WAO station (AMOF_20230725172925). COUNTICE-pt1 and COUNTICE-pt2 acknowledge funding support from the European Research Council (862565) and financial support from the University of Leeds International Strategy Fund. The deployment of PINE during the ExINP-ZEP campaign was supported by Stockholm University program financed by Swedish Environmental Protection Agency and ACTRIS-Sweden. KE and MIG acknowledge partial support by the project “PANhellenic infrastructure for Atmospheric Composition and climatE change” (MIS 5021516) co-financed by Greece and the European Union (European Regional Development Fund) in relation to the CALISHTO campaign.</p></ack><notes notes-type="financialsupport"><title>Financial support</title>

      <p id="d2e5423">This research has been supported by the Natural Environment Research Council (grant nos. NE/T00648X/1, NE/S00579X/1, NE/T006420/1, and NE/Y005376/1), the Biological and Environmental Research (grant no. DE-SC-0018979), the Agence Nationale de la Recherche (grant nos. ANR-21-CE01-0003, ANR-21-ESRE-0013, and ANR-21-CE01-0003), the Research Council of Finland (grant no. 337552), the H2020 European Research Council (grant nos. 871115 and 862565), the LIFE programme (grant no. CCA/GR/001747), the Directorate for Geosciences (grant nos. 1749851 and 2054847), the German Federal Ministry of Education and Research (BMBF, project number 57572852), and “PANhellenic infrastructure for Atmospheric Composition and climatE change” (MIS 5021516) co-financed by Greece and the European Union (European Regional Development Fund).</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d2e5430">This paper was edited by Montserrat Costa Surós and reviewed by two anonymous referees.</p>
  </notes><ref-list>
    <title>References</title>

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